"Signal-To-Noise Ratio" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
The comparison of the quantity of meaningful data to the irrelevant or incorrect data.
| Descriptor ID |
D059629
|
| MeSH Number(s) |
E05.318.740.872.875 E05.318.780.800.875 G17.800.500 N05.715.360.750.725.750 N05.715.360.780.700.840 N06.850.520.445.800.875 N06.850.520.830.872.750
|
| Concept/Terms |
Signal-To-Noise Ratio- Signal-To-Noise Ratio
- Ratio, Signal-To-Noise
- Ratios, Signal-To-Noise
- Signal To Noise Ratio
- Signal-To-Noise Ratios
|
Below are MeSH descriptors whose meaning is more general than "Signal-To-Noise Ratio".
Below are MeSH descriptors whose meaning is more specific than "Signal-To-Noise Ratio".
This graph shows the total number of publications written about "Signal-To-Noise Ratio" by people in this website by year, and whether "Signal-To-Noise Ratio" was a major or minor topic of these publications.
To see the data from this visualization as text,
click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 2013 | 0 | 2 | 2 |
| 2014 | 0 | 2 | 2 |
| 2015 | 0 | 2 | 2 |
| 2016 | 0 | 1 | 1 |
| 2017 | 0 | 2 | 2 |
| 2018 | 0 | 4 | 4 |
| 2019 | 0 | 1 | 1 |
| 2022 | 0 | 1 | 1 |
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click here.
Below are the most recent publications written about "Signal-To-Noise Ratio" by people in Profiles.
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Yonezawa H, Ueda D, Yamamoto A, Kageyama K, Walston SL, Nota T, Murai K, Ogawa S, Sohgawa E, Jogo A, Kabata D, Miki Y. Maskless 2-Dimensional Digital Subtraction Angiography Generation Model for Abdominal Vasculature using Deep Learning. J Vasc Interv Radiol. 2022 07; 33(7):845-851.e8.
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Song J, Chow HM, Nikam R, Kandula V, Choudhary AK, Li H. Reproducibility of axonal water fraction derived from the spherical mean diffusion weighted signal. Magn Reson Imaging. 2019 11; 63:49-54.
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Siman W, Mikell JK, Mawlawi OR, Mourtada F, Kappadath SC. Dose volume histogram-based optimization of image reconstruction parameters for quantitative 90 Y-PET imaging. Med Phys. 2019 Jan; 46(1):229-237.
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Li H, Chow HM, Chugani DC, Chugani HT. Minimal number of gradient directions for robust measurement of spherical mean diffusion weighted signal. Magn Reson Imaging. 2018 12; 54:148-152.
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Barrera CA, Otero HJ, White AM, Saul D, Biko DM. Depiction of the native coronary arteries during ECG-triggered High-Pitch Dual-Source Coronary Computed Tomography Angiography in children: Determinants of image quality. Clin Imaging. 2018 Nov - Dec; 52:240-245.
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Alizadeh M, Shah P, Conklin CJ, Middleton DM, Saksena S, Flanders AE, Krisa L, Mulcahey MJ, Faro SH, Mohamed FB. Application of Color Transformation Techniques in Pediatric Spinal Cord MR Images: Typically Developing and Spinal Cord Injury Population. J Digit Imaging. 2018 08; 31(4):543-552.
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Chilcott AK, Bradley KM, McGowan DR. Effect of a Bayesian Penalized Likelihood PET Reconstruction Compared With Ordered Subset Expectation Maximization on Clinical Image Quality Over a Wide Range of Patient Weights. AJR Am J Roentgenol. 2018 Jan; 210(1):153-157.
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Shimamoto S, Waldman ZJ, Orosz I, Song I, Bragin A, Fried I, Engel J, Staba R, Sharan A, Wu C, Sperling MR, Weiss SA. Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively. Clin Neurophysiol. 2018 01; 129(1):296-307.
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Siman W, Mawlawi OR, Mikell JK, Mourtada F, Kappadath SC. Effects of image noise, respiratory motion, and motion compensation on 3D activity quantification in count-limited PET images. Phys Med Biol. 2017 01 21; 62(2):448-464.
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Anvari A, Forsberg F, Samir AE. A Primer on the Physical Principles of Tissue Harmonic Imaging. Radiographics. 2015 Nov-Dec; 35(7):1955-64.